Global trends and academic landscapes of AI applications in basal cell carcinoma research: a bibliometric analysis
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By
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Yicheng Li
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Yanping Bai
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Lina Asihaer
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May 11, 2026
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Clinical Scorecard: Worldwide Patterns and Scholarly Insights on AI Utilization in Basal Cell Carcinoma Research: A Bibliometric Study
At a Glance
| Category | Detail |
|---|
| Condition | Basal Cell Carcinoma (BCC) |
| Key Mechanisms | Artificial Intelligence (AI) for early detection, risk stratification, and treatment decision-making. |
| Target Population | Individuals at risk for or diagnosed with Basal Cell Carcinoma. |
| Care Setting | Clinical dermatology and oncology practices. |
Key Highlights
- AI research in BCC has rapidly expanded since 2019.
- The United States and China are leading in BCC-related AI publications.
- Deep learning techniques are central to AI applications in BCC diagnosis.
- AI systems can match or exceed the diagnostic accuracy of dermatologists.
- Future studies should focus on multicenter validation of AI systems.
Guideline-Based Recommendations
Diagnosis
- Utilize AI-driven tools for improved diagnostic accuracy in BCC.
Management
- Incorporate AI systems into treatment planning and risk assessment.
Monitoring & Follow-up
- Regularly evaluate the performance of AI tools in clinical settings.
Risks
- Be aware of potential biases in AI algorithms and ensure diverse training datasets.
Patient & Prescribing Data
Patients with Basal Cell Carcinoma or at high risk for skin cancers.
AI can assist in personalized treatment planning and monitoring recurrence risk.
Clinical Best Practices
- Implement AI tools to enhance early detection of BCC.
- Ensure histopathological examination for ambiguous lesions.
- Promote interdisciplinary collaboration for AI integration in clinical practice.
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